Radar-based Drone Track-Before-Detect and Characteristics Estimation

Master thesis (2022)

Authors

X. Li Electrical Engineering, Mathematics and Computer Science

Contributors

Alexander Yarovoy Microwave Sensing, Signals & Systems - EEMCS (supervisor 1)
O.A. Krasnov Microwave Sensing, Signals & Systems - EEMCS (supervisor 2)
R.T. Rajan Signal Processing Systems - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Xingzhuo Li
More Info
expand_more

Published Date

24-08-2022

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Powered by technological advances, commercial drones have wide applications in areas such as photography, search, and rescue. However, the popularity of drones has raised technical and safety challenges for drone management and supervision. Traditional detect-before-track sensing approaches have reduced performance due to drones' low radar cross section, low velocity, and high maneuverability, and a long integration time is required to perform the spectrogram-based drone characteristics estimation.
In this thesis, a coherent electromagnetic scattering model of the drone is applied to the track-before-detect algorithm to provide a better detection performance in low signal-to-noise ratio cases and jointly estimate the dynamic state of the drone, including range, velocity, rotation frequency, and signal intensity from drone body and rotors. With the help of tracking results, a fusion of spectrogram-based characteristics estimation approaches is developed to estimate the constructional parameters of the drone, and a novel model-based number of rotor and multi-rotation frequency estimation method is proposed. The algorithms are first verified with simulation data, achieving 85%-95% detection probability at the SNR level below 5 dB and an estimation accuracy up to 96% in the number of rotor estimation. The algorithms are also validated with the experimental data, achieving agreement with the estimation results.